Multivariate Regression-Based Fault Detection and Recovery of UAV Flight Data

• Published in: IEEE transactions on instrumentation and measurement Vol. 69; no. 6; pp. 3527 - 3537
• Main Authors: Wang, Benkuan, Liu, Datong, Peng, Yu, Peng, Xiyuan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aircraft accidents & safety; Computer simulation; Data models; Data recovery; Fault detection; Fault detection (FD); fault recovery; Feature extraction; Gyroscopes; long short-term memory (LSTM); Mathematical model; Monitoring; Multivariate analysis; multivariate regression; Parameter estimation; Random noise; Regression models; Schedules; Statistical analysis; unmanned aerial vehicle (UAV); Unmanned aerial vehicles

With the wide applications of the unmanned aerial vehicle (UAV), operating safety becomes a critical issue. Thus, fault detection (FD) has been focused, which can realize fault alarm and schedule maintenance in time. Since the accurate physical model of UAV is usually difficult to obtain and flight...